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This study overviews machine learning applications for analyzing resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. It categorizes unsupervised and supervised methods, aiding researchers in this rapidly growing field.

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Area of Science:

  • Neuroscience
  • Computer Science
  • Data Science

Background:

  • Machine learning is increasingly used for analyzing resting-state functional Magnetic Resonance Imaging (rs-fMRI) data.
  • Understanding these applications is crucial for advancing neuroscience research.

Purpose of the Study:

  • To provide a comprehensive overview of machine learning applications in rs-fMRI analysis.
  • To systematically categorize unsupervised and supervised machine learning methods used with rs-fMRI data.

Main Methods:

  • A methodical taxonomy of machine learning techniques applied to rs-fMRI.
  • Classification of unsupervised learning into spatial, temporal, and population-based variation discovery.
  • Survey of algorithms and feature representations for supervised subject-level predictions.

Main Results:

  • Identification of three primary categories for unsupervised learning in rs-fMRI.
  • Detailed examination of successful algorithms and feature engineering for supervised rs-fMRI analysis.
  • A structured framework for understanding machine learning's role in rs-fMRI.

Conclusions:

  • Machine learning offers powerful tools for exploring complex rs-fMRI data.
  • This overview serves as a guide to the diverse landscape of ML applications in rs-fMRI.
  • The field of rs-fMRI analysis is rapidly evolving with machine learning advancements.